fit_hrg {igraph}

Fit a hierarchical random graph model

Description

fit_hrg() fits a HRG to a given graph. It takes the specified steps number of MCMC steps to perform the fitting, or a convergence criteria if the specified number of steps is zero. fit_hrg() can start from a given HRG, if this is given in the hrg() argument and the start argument is TRUE. It can be converted to the hclust class using as.hclust() provided in this package.

Usage

fit_hrg(graph, hrg = NULL, start = FALSE, steps = 0)

Arguments

graph

The graph to fit the model to. Edge directions are ignored in directed graphs.

hrg

A hierarchical random graph model, in the form of an igraphHRG object. fit_hrg() allows this to be NULL, in which case a random starting point is used for the fitting.

start

Logical, whether to start the fitting/sampling from the supplied igraphHRG object, or from a random starting point.

steps

The number of MCMC steps to make. If this is zero, then the MCMC procedure is performed until convergence.

Value

fit_hrg() returns an igraphHRG object. This is a list with the following members:

left

Vector that contains the left children of the internal tree vertices. The first vertex is always the root vertex, so the first element of the vector is the left child of the root vertex. Internal vertices are denoted with negative numbers, starting from -1 and going down, i.e. the root vertex is -1. Leaf vertices are denoted by non-negative number, starting from zero and up.

right

Vector that contains the right children of the vertices, with the same encoding as the left vector.

prob

The connection probabilities attached to the internal vertices, the first number belongs to the root vertex (i.e. internal vertex -1), the second to internal vertex -2, etc.

edges

The number of edges in the subtree below the given internal vertex.

vertices

The number of vertices in the subtree below the given internal vertex, including itself.

References

A. Clauset, C. Moore, and M.E.J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008);

A. Clauset, C. Moore, and M.E.J. Newman. Structural Inference of Hierarchies in Networks. In E. M. Airoldi et al. (Eds.): ICML 2006 Ws, Lecture Notes in Computer Science 4503, 1–13. Springer-Verlag, Berlin Heidelberg (2007).

See Also

Other hierarchical random graph functions: consensus_tree(), hrg(), hrg-methods, hrg_tree(), predict_edges(), print.igraphHRG(), print.igraphHRGConsensus(), sample_hrg()

Examples

## Not run: 
## We are not running these examples any more, because they
## take a long time (~15 seconds) to run and this is against the CRAN
## repository policy. Copy and paste them by hand to your R prompt if
## you want to run them.

## A graph with two dense groups
g <- sample_gnp(10, p = 1 / 2) + sample_gnp(10, p = 1 / 2)
hrg <- fit_hrg(g)
hrg
summary(as.hclust(hrg))

## The consensus tree for it
consensus_tree(g, hrg = hrg, start = TRUE)

## Prediction of missing edges
g2 <- make_full_graph(4) + (make_full_graph(4) - path(1, 2))
predict_edges(g2)

## End(Not run)